Recently, the adoption of a control theoretic perspective has started to provide quantitative insights on how functional brain states can be predicted by the underlying brain network structure[1]}.
In particular, network controllability - i.e., the theoretical ability to guide a system's state by operating on few driver nodes - has received a growing interest in several biological applications [2]}, [3]}, [4]}, [5]}.
Controllability of brain networks has been specifically explored to understand how the brain is able to endogenously modify its dynamics and if it is possible to steer it from exogenus stimuli [6]}, [7]}.
Hence, a crucial step in network controllability is to determine the nodes that are the best-suited to drive the system's state.
To this end, centrality measures based on controllability were developed and applied to brain networks for identifying the best nodes with the potential to steer the whole-brain activity [1]}, [9]}.
These studies might have practical implications because they can inform possible intervention strategies to favor specific patterns of behavior or treat brain diseases, by means of brain stimulation technology [10]}, [11]}, [6]}.
